Autonomous path planning requires a synergy between global reasoning and geometric precision, especially in complex or cluttered environments. While classical A* is valued for its optimality, it incurs prohibitive computational and memory costs in large-scale scenarios. Recent attempts to mitigate these limitations by using Large Language Models for waypoint guidance remain insufficient, as they rely only on text-based reasoning without spatial grounding. As a result, such models often produce incorrect waypoints in topologically complex environments with dead ends, and lack the perceptual capacity to interpret ambiguous physical boundaries. These inconsistencies lead to costly corrective expansions and undermine the intended computational efficiency. We introduce MMP-A*, a multimodal framework that integrates the spatial grounding capabilities of vision-language models with a novel adaptive decay mechanism. By anchoring high-level reasoning in physical geometry, the framework produces coherent waypoint guidance that addresses the limitations of text-only planners. The adaptive decay mechanism dynamically regulates the influence of uncertain waypoints within the heuristic, ensuring geometric validity while substantially reducing memory overhead. To evaluate robustness, we test the framework in challenging environments characterized by severe clutter and topological complexity. Experimental results show that MMP-A* achieves near-optimal trajectories with significantly reduced operational costs, demonstrating its potential as a perception-grounded and computationally efficient paradigm for autonomous navigation.

翻译：自主路径规划需要全局推理与几何精度的协同，尤其是在复杂或杂乱环境中。尽管经典A*算法因其最优性而备受推崇，但在大规模场景中会产生极高的计算和内存开销。近期利用大语言模型进行航点引导以缓解这些局限的尝试仍显不足，因为它们仅依赖基于文本的推理而缺乏空间基础。因此，这类模型在具有死路的拓扑复杂环境中常生成错误航点，且缺乏解析模糊物理边界的感知能力。这些不一致性导致代价高昂的修正扩展，并损害了预期的计算效率。我们提出MMP-A*，这是一个多模态框架，它将视觉-语言模型的空间基础能力与一种新颖的自适应衰减机制相结合。通过将高层推理锚定于物理几何，该框架生成连贯的航点引导，解决了纯文本规划器的局限性。自适应衰减机制动态调节启发函数中不确定航点的影响，在确保几何有效性的同时显著降低内存开销。为评估鲁棒性，我们在以严重杂乱和拓扑复杂性为特征的挑战性环境中测试该框架。实验结果表明，MMP-A*以显著降低的运算成本实现了接近最优的轨迹，证明了其作为一种基于感知且计算高效的自主导航范式的潜力。